CN117932989A - Thunderstorm path prediction method based on Bezier curve - Google Patents
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Abstract
The invention discloses a thunderstorm path prediction method based on Bezier curves, which comprises the following steps: s1, a Cartesian coordinate system is established by taking a radar as an origin, the position of a three-dimensional centroid of a thunderstorm monomer is identified, and the position is projected onto a plane of a two-dimensional coordinate system; s2, taking the two-dimensional coordinates of the barycenter of the thunderstorm monomers as a history sample, and obtaining a prediction result of the movement direction of the thunderstorm monomers according to the historic barycenter coordinates; s3, taking the shortest moving distance between adjacent time centroids in the historical sample as the moving distance of each adjacent time thunderstorm monomer in the forecasting result; s4, calculating a thunderstorm monomer moving path of the next time in the future according to the historical thunderstorm centroid, the thunderstorm monomer moving direction fitted by Bezier curves and the thunderstorm monomer moving distance of the adjacent time; s5, repeating the steps S1-S4, and after the historical sample is added, adjusting a thunderstorm path through a Bezier curve to update a prediction result in real time, wherein the method can rapidly predict the centroid position of the thunderstorm monomer within 0-120 minutes according to the historical centroid position of the thunderstorm monomer.
Description
Technical Field
The invention relates to the field of lightning characteristic observation and early warning analysis, in particular to a thunderstorm path prediction method based on a Bezier curve.
Background
Thunderstorms often pose a significant threat to human life and property through lightning, strong precipitation, hail, and strong winds. These hazards are highly localized, change rapidly, and affect time varies from minutes to hours, so it is difficult to accurately predict them using numerical weather forecast (NWP) patterns. Numerical weather forecast patterns can generally forecast the general trend of thunderstorms in a certain area, but cannot accurately forecast the time and location of occurrence of thunderstorms. Therefore, a better method is to make a neighbor forecast and disaster weather early warning according to the echo condition detected by the radar. The approach forecast predicts the recent (0-1 hour) weather condition according to the latest weather data, especially the observed data.
Since the 60 s of the 20 th century, various thunderstorm tracking and forecasting systems have been developed; these systems typically use mainly radar, but sometimes also in combination with other information, such as lightning data and satellite data. Thunderstorm identification, tracking and forecasting system TITAN is a particularly widely used radar echo based system that defines thunderstorms as continuous areas of high radar reflectivity. Radar-based proximity prediction methods include Cell Model Output Statistics, track 3D, thunderstorm RADAR TRACKING, and NowCastMIX, where radar and lightning data are used in combination. Other algorithms aim to make use of satellite data; prominent examples of which include GOES-R convection triggering; nowcasting Satellite Application Facility (NWCSAF) to develop a thunderstorm algorithm.
As with many other areas, the forecasting of thunderstorms and related disasters also benefits from the rapid development of machine learning techniques over the past decade. Machine learning has become a popular technique for precipitation prediction and has also been applied to the prediction of thunderstorms, and has also been used to develop predictions of lightning, hail, and thunderstorm winds. However, studies to date have generally used only one data source, and even if multiple data sources are used in some cases, most of the studies have focused on predicting only one variable. The diversity of the methods employed complicates the comparison between different study results. Meanwhile, since machine-learned thunderstorm prediction requires a large number of marked samples so as to be used for training a prediction model, the problems of difficult data acquisition and incomplete sample representativeness exist on the implementation level of specific application, and the current research on thunderstorm path prediction is relatively less.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a thunderstorm path prediction method based on a Bezier curve, which can predict the centroid position of a thunderstorm monomer within 0-120 minutes according to the historical centroid position of the thunderstorm monomer.
In order to achieve the above purpose, the invention adopts the following technical scheme:
A thunderstorm path prediction method based on Bezier curves comprises the following steps:
S1, a Cartesian coordinate system is established by taking a radar as an origin, the position of a three-dimensional centroid of a thunderstorm monomer is identified, and the three-dimensional centroid is projected onto a plane of a two-dimensional coordinate system;
S2, taking the two-dimensional coordinates of the barycenter of the thunderstorm monomers as a historical sample, and fitting a smooth curve through a Bezier curve according to the historical barycenter coordinates to obtain a prediction result of the movement direction of the thunderstorm monomers;
s3, taking the shortest moving distance between adjacent time centroids in the historical sample as the moving distance of each adjacent time thunderstorm monomer in the forecasting result;
S4, calculating a thunderstorm monomer moving path of the next time in the future according to the historical thunderstorm centroid, the thunderstorm monomer moving direction fitted by Bezier curves and the thunderstorm monomer moving distance of the adjacent time;
S5, repeating the steps S1-S4, and after the historical sample is added, adjusting the thunderstorm path through the Bezier curve to update the prediction result in real time.
As a preferred embodiment of the present invention: the identification of the thunderstorm monomer in the S1 specifically comprises the following steps: and identifying the thunderstorm monomers by using the thunderstorm information in different time periods in the thunderstorm development time sequence.
As a preferred embodiment of the present invention: the step S3 is specifically as follows: and counting the moving distance of the barycenters of the thunderstorm monomers between adjacent time barycenters in the historical sample, and taking the shortest moving distance of the barycenters as the moving distance of each adjacent time thunderstorm monomer in the forecasting result of the thunderstorm monomers.
As a preferred embodiment of the present invention: the step S4 specifically comprises the following steps: and (3) taking the last-time thunderstorm monomer centroid position in the historical sample as a starting point of the movement of the thunderstorm monomer, taking the shortest centroid movement distance as an arc length, taking a curve fitted by a Bezier curve as a multiplicative function, solving the terminal point coordinate of the movement of the thunderstorm monomer, and repeating S4 by taking the terminal point coordinate as the starting point to obtain the movement paths of the thunderstorm monomer at all future times in a forecast period.
As a preferred embodiment of the present invention: the step S5 specifically comprises the following steps: after the historical sample is added, the Bezier curve is always in the smallest polygon containing all control points, and when the Bezier curve is applied to thunderstorm path prediction, the prediction result of the thunderstorm path is updated in real time along with the continuous addition of the historical sample.
Compared with the prior art, the invention has the following beneficial effects:
(1) Compared with the method for predicting the thunderstorm single motion path by exponential weighted linear extrapolation in the TITAN algorithm, the method for predicting the thunderstorm single motion path based on Bezier curve fitting accords with the actual situation of the thunderstorm single motion path, can better predict the future motion path and motion trend of the thunderstorm single, and has smaller error compared with the TITAN algorithm.
(2) When the method for predicting the thunderstorm path based on Bezier curve fitting is applied to different thunderstorm monomers, the timeliness and accuracy of the prediction result can be changed according to different thunderstorm monomer types. The simpler and more linear the motion path of the thunderstorm monomers, the higher the timeliness and the accuracy of the prediction result. Conversely, the more complex the motion path of the thunderstorm monomers, the lower the timeliness and accuracy of the predicted results when undergoing merging or splitting during the development of the thunderstorm monomers. However, whether the motion path of the thunderstorm monomer is complex or not, the prediction result of the thunderstorm path prediction method based on Bezier curve fitting within 0-60 minutes has higher confidence.
Drawings
FIG. 1 is a flowchart of a method for predicting a thunderstorm path based on a Bezier curve according to an embodiment of the present invention;
FIG. 2 is a graph showing the comparison of the predicted result of the linear extrapolation of the curve obtained by the third-order polynomial fitting as the thunderstorm monomer development path and the predicted result of the thunderstorm monomer path based on the Bezier curve;
FIG. 3 is a schematic diagram of a result of predicting a thunderstorm path based on a Bezier curve according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the prediction results of predicting thunderstorm monomers alone according to the example of the present invention;
FIG. 5 is a schematic diagram of the predicted outcome of the thunderstorm monomer splitting prediction provided by the example of the present invention;
FIG. 6 is a schematic diagram of the prediction results of the two thunderstorm monomers according to the embodiment of the present invention.
Detailed Description
The present application is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the application and not limiting of its scope, and various equivalent modifications to the application will fall within the scope of the application as defined in the appended claims after reading the application.
Fig. 1-6 show a thunderstorm path prediction method based on Bezier curves, which comprises the following steps:
s1, a Cartesian coordinate system is established by taking a radar as an origin, the position of a three-dimensional centroid of a thunderstorm monomer is identified, and the three-dimensional centroid is projected onto a plane of a two-dimensional coordinate system; the identification of thunderstorm monomers is specifically as follows: identifying thunderstorm monomers by using thunderstorm information in different time periods in a thunderstorm development time sequence;
S2, taking the two-dimensional coordinates of the barycenter of the thunderstorm monomers as a historical sample, and fitting a smooth curve through a Bezier curve according to the historical barycenter coordinates to obtain a prediction result of the movement direction of the thunderstorm monomers;
S3, taking the shortest moving distance between adjacent time centroids in the historical sample as the moving distance of each adjacent time thunderstorm monomer in the forecasting result; counting the moving distance of the barycenters of the thunderstorm monomers between adjacent time barycenters in the historical sample, and taking the shortest moving distance of the barycenters as the moving distance of each adjacent time thunderstorm monomer in the forecasting result of the thunderstorm monomers;
S4, calculating a thunderstorm monomer moving path of the next time in the future according to the historical thunderstorm centroid, the thunderstorm monomer moving direction fitted by Bezier curves and the thunderstorm monomer moving distance of the adjacent time; taking the last time of the barycenter position of the thunderstorm monomer in the history sample as the starting point of the movement of the thunderstorm monomer, taking the shortest barycenter movement distance as the arc length, taking a curve fitted by a Bezier curve as a product function, solving the terminal point coordinate of the movement of the thunderstorm monomer, and repeating S4 by taking the terminal point coordinate as the starting point to obtain the movement paths of all the thunderstorm monomers in the future time within a forecast period;
S5, repeating the steps S1-S4, after the historical sample is added, adjusting the thunderstorm path to update the prediction result in real time through the Bezier curve, and after the historical sample is added, the Bezier curve is always in the smallest polygon containing all control points, and when the Bezier curve is applied to the thunderstorm path prediction, the prediction result of the thunderstorm path is updated in real time along with the continuous addition of the historical sample.
Example 1
FIG. 1 is a flowchart of a method for predicting a thunderstorm path based on a Bezier curve according to an embodiment of the present invention, which specifically includes the following steps:
Step 1, specifically taking radar-based data of a complete thunderstorm weather process observed by weather radar of CINRAD/SA in Guangdong, 5 th month and 5 th day of 2016:00-8:00 UTC, guangzhou Huangpu, etc. as an example, using reflectivity data in the radar-based data as original data for identifying a thunderstorm system. And establishing a Cartesian coordinate system by taking the radar station position as an origin and the positive direction of the y-axis as the north direction. And selecting the thunderstorm information of 4:00-5:18,4:00-6:18 and 4:00-6:48 times in the thunderstorm development time sequence to identify the thunderstorm monomers, respectively identifying the mass center positions of the three-dimensional thunderstorm monomers in the three time periods, and projecting the mass center positions into a two-dimensional coordinate system.
And 2, respectively taking mass center positions of 4:00-5:18 of thunderstorm monomer samples, and fitting a movement direction of the thunderstorm monomer according to a Bezier curve according to the historic thunderstorm monomer mass center samples, so as to obtain a predicted result of the movement direction of the thunderstorm for 120 minutes as shown in fig. 4.
And 3, taking the shortest moving distance between the centers of mass of adjacent secondary thunderstorm monomers in the historical thunderstorm monomer samples in the time period of 4:00-5:18 as the moving distance of each adjacent secondary thunderstorm monomer in the forecasting result.
And 4, according to the last time of the centroid positions of the historical thunderstorm monomer samples in the time period of 4:00-5:18, taking the centroid position of the thunderstorm monomer as a starting point, taking the shortest centroid moving distance as an arc length, taking a curve fitted by a Bezier curve as a multiplicative function, and obtaining an end point coordinate through a curve integral formula. And (3) repeating the step (4) by taking the end point coordinates as a starting point to obtain all the moving paths of the thunderstorm monomers within 120 minutes in the future.
And 5, respectively taking a thunderstorm monomer sample 4:00-5:18 time period, a thunderstorm monomer sample 4:00-6:18 time period and a center of mass position of the thunderstorm monomer in a two-dimensional coordinate system in the thunderstorm monomer sample 4:00-6:48 time period, and repeating the steps 1-4 according to the historical center of mass positions of the thunderstorm monomer in the three time periods to obtain a predicting result of the movement direction of the thunderstorm monomer and a predicting result of the movement distance of the thunderstorm monomer in the adjacent time period by fitting a Bezier curve in the three time periods, so as to finally obtain a predicting result of the movement path of the thunderstorm monomer in 120 minutes, 60 minutes and 30 minutes.
As shown in FIG. 3, the thunderstorm path prediction result based on Bezier curve provided by the embodiment of the invention. Wherein a-f are the historical centroid samples added to the thunderstorm centroid coordinates continuously along with the time, and the thunderstorm path prediction result is adjusted continuously by the invention;
FIG. 4 shows the prediction results of the present invention when predicting a single thunderstorm monomer, wherein a-c are the prediction results of the thunderstorm monomer path, and d-f are the errors between the prediction results of the thunderstorm monomer path and the actual motion path of the thunderstorm monomer by using the linear extrapolation method and the Bezier curve fitting method;
As shown in fig. 5, a is a predicted result of the thunderstorm monomer splitting provided by the embodiment of the invention, wherein a is a predicted result of the thunderstorm monomer path before splitting, and b-c is an error between a result of predicting the thunderstorm monomer path by using a linear extrapolation method and a Bezier curve fitting method and an actual motion path of the thunderstorm monomer;
As shown in fig. 6, the prediction results of the two thunderstorm monomers in the combining process provided by the embodiment of the invention are shown in fig. 6, wherein a is the prediction result of the paths of the first two thunderstorm monomers, and b-c is the error between the result of predicting the paths of the two thunderstorm monomers and the actual motion paths of the two thunderstorm monomers by using the linear extrapolation method and the Bezier curve fitting method;
Compared with the exponential weighted linear extrapolation prediction thunderstorm monomer motion path in the TITAN algorithm, the prediction method based on Bezier curve fitting thunderstorm monomer motion path better accords with the actual situation of the thunderstorm monomer motion path, and can better predict the future motion path and motion trend of the thunderstorm monomer, as shown in fig. 2, and compared with the TITAN algorithm, the error is smaller.
When the method for predicting the thunderstorm path based on Bezier curve fitting is applied to different thunderstorm monomers, the timeliness and accuracy of the prediction result can be changed according to different thunderstorm monomer types. The simpler and more linear the motion path of the thunderstorm monomers, the higher the timeliness and the accuracy of the prediction result. Conversely, the more complex the motion path of the thunderstorm monomers, the lower the timeliness and accuracy of the predicted results when undergoing merging or splitting during the development of the thunderstorm monomers. However, whether the motion path of the thunderstorm monomer is complex or not, the prediction result of the thunderstorm path prediction method based on Bezier curve fitting within 0-60 minutes has higher confidence.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (5)
1. A thunderstorm path prediction method based on Bezier curves is characterized by comprising the following steps:
S1, a Cartesian coordinate system is established by taking a radar as an origin, the position of a three-dimensional centroid of a thunderstorm monomer is identified, and the three-dimensional centroid is projected onto a plane of a two-dimensional coordinate system;
S2, taking the two-dimensional coordinates of the barycenter of the thunderstorm monomers as a historical sample, and fitting a smooth curve through a Bezier curve according to the historical barycenter coordinates to obtain a prediction result of the movement direction of the thunderstorm monomers;
s3, taking the shortest moving distance between adjacent time centroids in the historical sample as the moving distance of each adjacent time thunderstorm monomer in the forecasting result;
S4, calculating a thunderstorm monomer moving path of the next time in the future according to the historical thunderstorm centroid, the thunderstorm monomer moving direction fitted by Bezier curves and the thunderstorm monomer moving distance of the adjacent time;
S5, repeating the steps S1-S4, and after the historical sample is added, adjusting the thunderstorm path through the Bezier curve to update the prediction result in real time.
2. The method for predicting a thunderstorm path based on a Bezier curve according to claim 1, wherein the identification of the thunderstorm monomer in S1 is specifically as follows: and identifying the thunderstorm monomers by using the thunderstorm information in different time periods in the thunderstorm development time sequence.
3. The method for predicting a thunderstorm path based on a Bezier curve according to claim 1, wherein the step S3 is specifically: and counting the moving distance of the barycenters of the thunderstorm monomers between adjacent time barycenters in the historical sample, and taking the shortest moving distance of the barycenters as the moving distance of each adjacent time thunderstorm monomer in the forecasting result of the thunderstorm monomers.
4. The method for predicting a thunderstorm path based on a Bezier curve according to claim 1, wherein the S4 specifically is: and (3) taking the last-time thunderstorm monomer centroid position in the historical sample as a starting point of the movement of the thunderstorm monomer, taking the shortest centroid movement distance as an arc length, taking a curve fitted by a Bezier curve as a product function to obtain the terminal point coordinate of the movement of the thunderstorm monomer, and repeating S4 by taking the terminal point coordinate as the starting point to obtain the movement paths of all the thunderstorm monomers in the future time within a forecast period.
5. The method for predicting a thunderstorm path based on a Bezier curve according to claim 1, wherein the step S5 is specifically: after the historical sample is added, the Bezier curve is always in the smallest polygon containing all control points, and when the Bezier curve is applied to thunderstorm path prediction, the prediction result of the thunderstorm path is updated in real time along with the continuous addition of the historical sample.
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